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Rütger Rollenbeck
,
Katja Trachte
, and
Jörg Bendix

Abstract

Quality control is a particularly demanding problem for micrometeorological studies in complex environments. With the transition to electronic sensing and storage of climate data in high temporal resolution, traditional approaches of homogenization are insufficient for addressing the small-scale variability and spatial heterogeneity of the data. This problem can be successfully addressed by introducing a new class of control procedures based on the physical and climatological relations between different climate variables. The new approach utilizes knowledge about the interdependency of air temperature, precipitation, radiation, relative air humidity, cloud cover, and visibility to develop empirical functions for determining the probability margins for the co-occurrence of specific conditions in tropical mountains and deserts. It can also be applied to other geographic settings by adjusting the parameters derived from the data itself. All procedures are integrated into a processing chain with feedback loops and combined with conventional logical and statistical checks, which enables it to detect small errors that normally pass unnoticed. The algorithms are also adapted to incorporate the short time steps of the original data to retain the potential for detailed process analyses.

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Michaela Vorndran
,
Adrian Schütz
,
Jörg Bendix
, and
Boris Thies

Abstract

Large inaccuracies still exist in accurately predicting fog formation, dissipation, and duration. To improve these deficiencies, machine learning (ML) algorithms are increasingly used in nowcasting in addition to numerical fog forecasts because of their computational speed and their ability to learn the nonlinear interactions between the variables. Although a powerful tool, ML models require precise training and thoroughly evaluation to prevent misinterpretation of the scores. In addition, a fog dataset’s temporal order and the autocorrelation of the variables must be considered. Therefore, classification-based ML related pitfalls in fog forecasting will be demonstrated in this study by using an XGBoost fog forecasting model. By also using two baseline models that simulate guessing and persistence behavior, we have established two independent evaluation thresholds allowing for a more assessable grading of the ML model’s performance. It will be shown that, despite high validation scores, the model could still fail in operational application. If persistence behavior is simulated, commonly used scores are insufficient to measure the performance. That will be demonstrated through a separate analysis of fog formation and dissipation, because these are crucial for a good fog forecast. We also show that commonly used blockwise and leave-many-out cross-validation methods might inflate the validation scores and are therefore less suitable than a temporally ordered expanding window split. The presented approach provides an evaluation score that closely mimics not only the performance on the training and test dataset but also the operational model’s fog forecasting abilities.

Significance Statement

This study points out current pitfalls in the training and evaluation of pointwise radiation fog forecasting with machine learning algorithms. The objective of this study is to raise awareness of 1) consideration of the time stability of variables (autocorrelation) during training and evaluation, 2) the necessity of evaluating the performance of a fog forecasting model in direct comparison with an independent performance threshold (baseline model) that evaluates whether the fog forecasting model is better than guessing, and 3) the fact that prediction of fog formation and dissipation must be evaluated separately because a model that misses all of these transitions can still achieve high performance in the commonly used overall evaluation.

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Meike Kühnlein
,
Boris Thies
,
Thomas Nauß
, and
Jörg Bendix

Abstract

The potential of rainfall-rate assignment using Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Instrument (SEVIRI) data is investigated. For this purpose, a new conceptual model for precipitation processes in connection with midlatitude cyclones is developed, based on the assumption that high rainfall rates are linked to a high optical thickness and a large effective particle radius, whereas low rainfall rates are linked to a low optical thickness and a small effective particle radius. Reflection values in the 0.56–0.71-μm (VIS0.6) and 1.5–1.78-μm (NIR1.6) channels, which provide information about the optical thickness and the effective radius, are considered in lieu of the optical and microphysical cloud properties. An analysis of the relationship between VIS0.6 and NIR1.6 reflection and the ground-based rainfall rate revealed a high correlation between the sensor signal and the rainfall rate. Based on these findings, a method for rainfall-rate assignment as a function of VIS0.6 and NIR1.6 reflection is proposed. The validation of the proposed technique showed encouraging results, especially for temporal resolutions of 6 and 12 h. This is a significant improvement compared to existing IR retrievals, which obtain comparable results for monthly resolution. The existing relationship between the VIS0.6 and NIR1.6 reflection values and the ground-based rainfall rate is corroborated with the new conceptual model. The good validation results indicate the high potential for rainfall retrieval in the midlatitudes with the high spatial and temporal resolution provided by MSG SEVIRI.

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Jörg Bendix
,
Boris Thies
,
Jan Cermak
, and
Thomas Nauß

Abstract

The distinction made by satellite data between ground fog and low stratus is still an open problem. A proper detection scheme would need to make a determination between low stratus thickness and top height. Based on this information, stratus base height can be computed and compared with terrain height at a specific picture element. In the current paper, a procedure for making the distinction between ground fog and low-level stratus is proposed based on Moderate Resolution Imaging Spectroradiometer (MODIS, flying on board the NASA Terra and Aqua satellites) daytime data for Germany. Stratus thickness is alternatively derived from either empirical relationships or a newly developed retrieval scheme (lookup table approach), which relies on multiband albedo and radiative transfer calculations. A trispectral visible–near-infrared (VIS–NIR) approach has been proven to give the best results for the calculation of geometrical thickness. The comparison of horizontal visibility data from synoptic observing (SYNOP) stations of the German Weather Service and the results of the ground fog detection schemes reveals that the lookup table approach shows the best performance for both a valley fog situation and an extended layer of low stratus with complex local visibility structures. Even if the results are very encouraging [probability of detection (POD) = 0.76], relatively high percentage errors and false alarm ratios still occur. Uncertainties in the retrieval scheme are mostly due to possible collocation errors and known problems caused by comparing point and pixel data (time lag between satellite overpass and ground observation, etc.). A careful inspection of the pixels that mainly contribute to the false alarm ratio reveals problems with thin cirrus layers and the fog-edge position of the SYNOP stations. Validation results can be improved by removing these suspicious pixels (e.g., percentage error decreases from 28% to 22%).

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Jörg Bendix
,
Katja Trachte
,
Jan Cermak
,
Rütger Rollenbeck
, and
Thomas Nauß

Abstract

This study examines the seasonal and diurnal dynamics of convective cloud entities—small cells and a mesoscale convective complex–like pattern—in the foothills of the tropical eastern Andes. The investigation is based on Geostationary Operational Environmental Satellite-East (GOES-E) satellite imagery (2005–07), images of a scanning X-band rain radar, and data from regular meteorological stations. The work was conducted in the framework of a major ecological research program, the Research Unit 816, in which meteorological instruments are installed in the Rio San Francisco valley, breaching the eastern Andes of south Ecuador. GOES image segmentation to discriminate convective cells and other clouds is performed for a 600 × 600 km2 target area, using the concept of connected component labeling by applying the 8-connectivity scheme as well as thresholds for minimum blackbody temperature, spatial extent, and eccentricity of the extracted components. The results show that the formation of convective clouds in the lowland part of the target area mainly occurs in austral summer during late afternoon. Nocturnal enhancement of cell formation could be observed from October to April (particularly February–April) between 0100 and 0400 LST (LST = UTC − 5 h) in the Andean foothill region of the target area, which is the relatively dry season of the adjacent eastern Andean slopes. Nocturnal cell formation is especially marked southeast of the Rio San Francisco valley in the southeast Andes of Ecuador, where a confluence area of major katabatic outflow systems coincide with a quasi-concave shape of the Andean terrain line. The confluent cold-air drainage flow leads to low-level instability and cellular convection in the warm, moist Amazon air mass. The novel result of the current study is to provide statistical evidence that, under these special topographic situations, katabatic outflow is strong enough to generate mainly mesoscale convective complexes (MCCs) with a great spatial extent. The MCC-like systems often increase in expanse during their mature phase and propagate toward the Andes because of the prevailing upper-air easterlies, causing early morning peaks of rainfall in the valley of the Rio San Francisco. It is striking that MCC formation in the foothill area is clearly reduced during the main rainy season [June–August (JJA)] of the higher eastern Andean slopes. At a first glance, this contradiction can be explained by rainfall persistence in the Rio San Francisco valley, which is clearly lower during the time of convective activity (December–April) in comparison with JJA, during which low-intensity rainfall is released by predominantly advective clouds with greater temporal endurance.

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Johanna Orellana-Alvear
,
Rolando Célleri
,
Rütger Rollenbeck
, and
Jörg Bendix

Abstract

Information on the spatiotemporal rainfall occurrence, its microphysical characteristics, and its reflectivity–rainfall (ZR) relations required to provide rainfall mapping based on rain radar data is limited for tropical high mountains. Therefore, this study aims to analyze rainfall types in the Andes cordillera to derive different rain-type ZR relations using disdrometer observations at three study sites representative for different geographic positions and elevations (2610, 3626, and 3773 m MSL). Rain categorization based on mean drop volume diameter (D m ) thresholds [0.1 < D m (mm) ≤ 0.5; 0.5 < D m (mm) ≤ 1.0; 1.0 < D m (mm) ≤ 2.0] was performed using drop size distribution data at a 5-min time step over an approximate 2-yr period at each location. The findings are as follows: (i) Rain observations characterized by higher (lower) D m and rain rates are more frequent at the lower (higher) site. (ii) Because of its geographic position, very light rain (drizzle) is more common at higher altitudes with longer-duration events, whereas rainfall is more convective at the lower range. (iii) The specific spatial exposition regarding cloud and rain formation seems to play an important role for derivation of the local ZR relationship. (iv) Low A coefficients (≤60) for the first rain type resemble typical characteristics of orographic precipitation. (v) Greater values of A (lowest and highest stations for D m > 1.0 mm) are attributed to transitional rainfall as found in other studies. (vi) Rain-type ZR relations show a better adjustment in comparison with site-specific ZR relationships. This study is the first contribution of ZR relations for tropical rainfall in the high Andes.

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Jörg Bendix
,
Andreas Fries
,
Jorge Zárate
,
Katja Trachte
,
Rütger Rollenbeck
,
Franz Pucha-Cofrep
,
Renzo Paladines
,
Ivan Palacios
,
Johanna Orellana
,
Fernando Oñate-Valdivieso
,
Carlos Naranjo
,
Leonardo Mendoza
,
Diego Mejia
,
Mario Guallpa
,
Francisco Gordillo
,
Victor Gonzalez-Jaramillo
,
Maik Dobbermann
,
Rolando Célleri
,
Carlos Carrillo
,
Augusto Araque
, and
Sebastian Achilles

Abstract

Weather radar networks are indispensable tools for forecasting and disaster prevention in industrialized countries. However, they are far less common in the countries of South America, which frequently suffer from an underdeveloped network of meteorological stations. To address this problem in southern Ecuador, this article presents a novel radar network using cost-effective, single-polarization, X-band technology: the RadarNet-Sur. The RadarNet-Sur network is based on three scanning X-band weather radar units that cover approximately 87,000 km2 of southern Ecuador. Several instruments, including five optical disdrometers and two vertically aligned K-band Doppler radar profilers, are used to properly (inter) calibrate the radars. Radar signal processing is a major issue in the high mountains of Ecuador because cost-effective radar technologies typically lack Doppler capabilities. Thus, special procedures were developed for clutter detection and beam blockage correction by integrating ground-based and satelliteborne measurements. To demonstrate practical applications, a map of areas frequently affected by intense rainfall is presented, based on a time series of one radar that has been in operation since 2002. Such information is of vital importance to, for example, infrastructure management because rain-driven landslides are a major issue for road maintenance and safety throughout Ecuador. The presented case study of exceptionally strong rain events during the recent El Niño in March 2015 highlights the system’s practicality in weather forecasting related to disaster management. For the first time, RadarNet-Sur warrants a spatial-explicit observation of El Niño-related heavy precipitation in a transect from the coast to the highlands in a spatial resolution of 500 m.

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